The present invention relates to digital signal processing and in particular to signal detection.
When data packets are being transmitted over a mobile radio channel, multipath propagation occurs, and causes intersymbol interference with the signal. The intersymbol interference is normally corrected by a combination of a linear equalizer and a Viterbi algorithm (see “Equalization Concepts for EDGE” by W. H. Gerstacker, R. Schober, IEEE Trans. Wireless Comm., pages 190-199, January 2002, and US 2001/0050967 A1).
In addition to the intersymbol interference, the received signal generally also has superimposed on it various other types of interference, such as noise, co-channel interference and adjacent-channel interference. While the interference in the case of co-channel interference is in the same frequency band as the useful signal, and, for example, is caused by a subscriber who is active in another cell in the network, the interference in the case of adjacent-channel interference occurs in one of the two adjacent frequency bands.
Adjacent-channel interference is influenced by the channel bandwidth and the symbol frequency used in the system. A narrow channel bandwidth and a high symbol frequency are desirable in order to achieve a high system subscriber capacity and a high data rate. On the other hand, this results in an increase in the adjacent-channel interference, which must not exceed a specific limit.
In many mobile communication systems, such as GSM (Global System for Mobile Communication) and its further development EDGE (Enhanced Data Services for GSM Evolution) the overall transmission bandwidth is subdivided into a large number of narrowband frequency bands (traffic channels). For GSM and EDGE, the symbol frequency is 270.833 kHz, and the channel bandwidth is 200 kHz. This means that the useful signal and the adjacent-channel interference spectrally overlap one another. It is impossible to completely suppress the adjacent-channel interference without constricting the spectrum of the useful signal.
DE 101 52 628 A1 (“Adaptives Kanalfilter für Mobilfunkempfänger und Verfahren zur adaptiven Filterung” [Adaptive channel filter for mobile radio receivers, and method for adaptive filtering] by X. Wu, B. Yang) and DE 102 53 671 (“Unterdrückung der Nachbarkanalinterferenz durch Kanalfilterung in Mobilfunkempfängern” [Suppression of the adjacent-channel interference by channel filtering in mobile radio receivers] by X. Wu, B. Gunzelmann) have proposed a method which adjusts the pass bandwidth of the channel filter as a function of the strength of the adjacent-channel interference. This results in an adaptive channel filter which allows optimum filtering of the received signal, in terms of the suppression of adjacent-channel interference, in different reception and/or interference situations. However, an adaptive filter such as this is not often the optimum solution for every interference situation. An optimum solution is achieved only when the optimum algorithm for interference reduction can be used for each interference type. However, this is dependent on reliable identification of the interference types.
In order to identify the interference, the channel coefficients are first of all estimated from the received signal using a known symbol sequence (training sequence). The following channel model is used for channel estimation:
                              x          ⁡                      (            k            )                          =                                            ∑                              i                =                0                            L                        ⁢                                          h                ⁡                                  (                  i                  )                                            ·                              t                ⁡                                  (                                      k                    -                    i                                    )                                                              +                      n            ⁡                          (              k              )                                                          (        1        )            
In this case, (t(0) . . . t(N−1)) is the training sequence with the length N, L is the order of the channel and (h(0) . . . h(L)) are the channel coefficients to be estimated. n(k) represents the noise plus interference and x(k) is the received signal.
The received signal x(k) is normally composed of a superimposition of the useful signal S, the noise N and the interference I. The signal S can be reconstructed from the estimated channel coefficients and the known training sequence. The difference between x(k) and the reconstructed signal results in an error signal e(k) which predominantly contains only noise and interference. The energy in the reconstructed signal PS and of the error signal PN+PI can be calculated from the sum of the squares of the magnitudes of the respective signal.
Since the mean energy of the noise PN is a receiver parameter and generally remains constant, while the interference energy changes from one burst to the next, it is possible to determine the noise energy PN and the interference PI from knowledge of the noise level in the receiver. The estimated signal-to-noise ratio SNR=PS/PN and the estimated signal-to-interference ratio SIR=PS/PI are used as measures for the identification of the interference. Interference is detected when the SNR-SIR exceeds a predefined threshold.
One disadvantage of this method is that it detects only the interference but does not distinguish between co-channel interference and adjacent-channel interference, and therefore does not make it possible to choose optimum interference reduction for the respective interference situation. Furthermore, the detection process is relatively susceptible to errors as a result of the short training sequence duration (for example 26 symbols) and the resultant inaccurate estimation of the energy.
The method proposed in WO 02/067444 A1 (“Apparatus for and method of reducing interference in a communications receiver” by A. Kleinermann et al.) likewise identifies the interference from the error signal which is derived from the channel estimation. The autocorrelation vector or the power density spectrum of the error signal is used as a measure. The autocorrelation vector of the error signal can be calculated as follows:
                                                        r              ee                        ⁡                          (              n              )                                =                                    ∑                              k                =                0                                            N                -                L                                      ⁢                                          e                ⁡                                  (                  k                  )                                            ·                              e                ⁡                                  (                                      k                    +                    n                    -                    N                    +                    L                                    )                                                                    ⁢                                  ⁢                  (                                    n              =              0                        ,            1            ,                          …              ⁢                                                          ⁢              2              ⁢                              (                                  N                  -                  L                                )                                              )                                    (        2        )            
The power density spectrum of the error signal can be calculated by Fourier transformation of the autocorrelation vector:
                                          R            ee                    ⁡                      (            k            )                          =                              ∑                          n              =              0                                      2              ⁢                              (                                  N                  -                  L                                )                                              ⁢                                                    r                ee                            ⁡                              (                n                )                                      ·                                          ⅇ                                                                            -                      j2π                                        ⁢                                                                                  ⁢                    kn                                                                              2                      ⁢                                              (                                                  N                          -                          L                                                )                                                              +                    1                                                              ⁢                                                          (                                                k                  =                  0                                ,                1                ,                                  …                  ⁢                                                                          ⁢                  2                  ⁢                                      (                                          N                      -                      L                                        )                                                              )                                                          (        3        )            
First of all, a large amount of received data relating to different channel types and interference types is recorded off-line. The corresponding power density spectra or autocorrelation vectors of the error signal are calculated off-line for each channel type and interference type, are averaged over a long time, and are then stored as references in the memory (RAM or ROM). During operation, the power density spectrum and/or the autocorrelation vector of the current burst are/is calculated on-line, and are/is compared with the references.
The first variant, which is illustrated in FIG. 4, uses the power density spectrum as the detection feature. In this case, a matched filter bank with N filters (N is the number of different interference references) is required. The matched filter with the maximum output is then taken, and the corresponding interference reference is chosen as the current interference type.
The second variant, which is illustrated in FIG. 5, uses the autocorrelation vector of the error signal for detection. The interference reference whose autocorrelation vector is closest to the current autocorrelation vector is taken as the current interference type.
The greater the number of different channel types and interference types used, the greater is the implementation complexity. Since the feature (power density spectrum or autocorrelation vector) of the current error signal can vary to a relatively major extent from one burst to the next as a result of multipower propagation, direct feature comparison of the current burst with the references does not always lead to a correct association.
For these and other reasons, there is a need for the present invention.